The risk of confusing model calibration and model validation with model acceptance
Bibliographic record
Abstract
This paper examines the meaning of calibration and validation in rock engineering design, highlighting several challenges and limitations associated with these processes. There exist two fundamental limitations: i) the inability to rely on engineering judgement as a substitute for proper calibration and validation, and ii) the use of qualitative characterisation methods introduces subjectivity in the data subsequently used for calibration and validation. Furthermore, varying modelling conceptualisations result in a paradoxical situation whereby the same problem analysed using different numerical models requires a different set of parameters, which can all be claimed to be calibrated. The author acknowledges that some of the points raised in this paper may encounter objections. However, by ignoring the epistemic limits of calibration and validation, there is the risk of letting engineering faith become the excuse behind the tendency to replace model validation with model acceptance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.234 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".